Data used for training a model might not be the same as during test time. This is a big problem for robust real world applications.
The sensitive attribute relates now to the different domains or environments [1]}. As seen in Sec. REF  we enforce to learn a more general feature representation and to ignore domain specific attributes. This is leveraged to learn representations which are generic across (related) domains and hence would generalize better.
